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import inspect |
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import torch |
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from einops import rearrange |
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from torch import nn |
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from torch.distributed.fsdp import FullyShardedDataParallel as FSDP |
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from .helpers import PerceiverResampler |
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def unwrap_fsdp(m): |
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if isinstance(m, FSDP): |
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return unwrap_fsdp(m.module) |
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return m |
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def accepts_parameter(func, parameter_name): |
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signature = inspect.signature(func) |
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return parameter_name in signature.parameters |
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class Flamingo(nn.Module): |
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def __init__( |
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self, |
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vision_encoder: nn.Module, |
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lang_encoder: nn.Module, |
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eoc_token_id: int, |
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media_token_id: int, |
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vis_dim: int, |
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cross_attn_every_n_layers: int = 1, |
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gradient_checkpointing: bool = False, |
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enable_init_network_params: bool = False, |
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initializer_range: float = 0.02, |
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): |
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""" |
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Args: |
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vision_encoder (nn.Module): HF CLIPModel |
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lang_encoder (nn.Module): HF causal language model |
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eoc_token_id (int): Token id for <|endofchunk|> |
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media_token_id (int): Token id for <image> |
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vis_dim (int): Dimension of the visual features. |
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Visual features are projected to match this shape along the last dimension. |
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cross_attn_every_n_layers (int, optional): How often to apply cross attention after transformer layer. Defaults to 1. |
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""" |
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super().__init__() |
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self.eoc_token_id = eoc_token_id |
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self.media_token_id = media_token_id |
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self.vis_dim = vis_dim |
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if hasattr(lang_encoder.config, "d_model"): |
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self.lang_dim = lang_encoder.config.d_model |
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else: |
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self.lang_dim = lang_encoder.config.hidden_size |
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self.vision_encoder = ( |
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vision_encoder.visual |
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if hasattr(vision_encoder, "visual") |
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else vision_encoder |
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) |
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self.perceiver = PerceiverResampler( |
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dim=self.vis_dim, |
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enable_init_network_params=enable_init_network_params, |
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initializer_range=initializer_range, |
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gradient_checkpointing=gradient_checkpointing, |
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) |
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self.lang_encoder = lang_encoder |
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self.lang_encoder.init_flamingo( |
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media_token_id=media_token_id, |
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lang_hidden_size=self.lang_dim, |
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vis_hidden_size=self.vis_dim, |
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cross_attn_every_n_layers=cross_attn_every_n_layers, |
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gradient_checkpointing=gradient_checkpointing, |
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enable_init_network_params=enable_init_network_params, |
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initializer_range=initializer_range, |
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) |
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self._use_gradient_checkpointing = gradient_checkpointing |
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self.perceiver._use_gradient_checkpointing = gradient_checkpointing |
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def forward( |
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self, |
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vision_x: torch.Tensor, |
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lang_x: torch.Tensor, |
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attention_mask: torch.Tensor = None, |
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labels: torch.Tensor = None, |
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clear_conditioned_layers: bool = True, |
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past_key_values=None, |
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use_cache: bool = False, |
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): |
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""" |
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Forward pass of Flamingo. |
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Args: |
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vision_x (torch.Tensor): Vision input |
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shape (B, T_img, F, C, H, W) with F=1 |
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lang_x (torch.Tensor): Language input ids |
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shape (B, T_txt) |
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attention_mask (torch.Tensor, optional): Attention mask. Defaults to None. |
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labels (torch.Tensor, optional): Labels. Defaults to None. |
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clear_conditioned_layers: if True, clear the conditioned layers |
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once the foward pass is completed. Set this to false if the |
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same set of images will be reused in another subsequent |
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forward pass. |
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past_key_values: pre-computed values to pass to language model. |
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See past_key_values documentation in Hugging Face |
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CausalLM models. |
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use_cache: whether to use cached key values. See use_cache |
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documentation in Hugging Face CausalLM models. |
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""" |
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assert ( |
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self.lang_encoder.initialized_flamingo |
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), "Flamingo layers are not initialized. Please call `init_flamingo` first." |
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assert ( |
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self.lang_encoder._use_cached_vision_x or vision_x is not None |
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), "Must provide either vision_x or have precached media using cache_media()." |
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if self.lang_encoder._use_cached_vision_x: |
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assert vision_x is None, ( |
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"Expect vision_x to be None when media has been cached using" |
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" cache_media(). Try uncache_media() first." |
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) |
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assert self.lang_encoder.is_conditioned() |
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else: |
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self._encode_vision_x(vision_x=vision_x) |
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self._condition_media_locations(input_ids=lang_x) |
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output = self.lang_encoder( |
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input_ids=lang_x, |
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attention_mask=attention_mask, |
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labels=labels, |
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past_key_values=past_key_values, |
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use_cache=use_cache, |
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) |
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if clear_conditioned_layers: |
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self.lang_encoder.clear_conditioned_layers() |
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return output |
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def generate( |
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self, |
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vision_x: torch.Tensor, |
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lang_x: torch.Tensor, |
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attention_mask: torch.Tensor = None, |
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**kwargs, |
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): |
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""" |
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Generate text conditioned on vision and language inputs. |
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Args: |
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vision_x (torch.Tensor): Vision input |
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shape (B, T_img, F, C, H, W) |
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images in the same chunk are collated along T_img, and frames are collated along F |
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currently only F=1 is supported (single-frame videos) |
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lang_x (torch.Tensor): Language input |
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shape (B, T_txt) |
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**kwargs: see generate documentation in Hugging Face CausalLM models. Some notable kwargs: |
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max_length (int, optional): Maximum length of the output. Defaults to None. |
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attention_mask (torch.Tensor, optional): Attention mask. Defaults to None. |
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num_beams (int, optional): Number of beams. Defaults to 1. |
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max_new_tokens (int, optional): Maximum new tokens. Defaults to None. |
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temperature (float, optional): Temperature. Defaults to 1.0. |
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top_k (int, optional): Top k. Defaults to 50. |
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top_p (float, optional): Top p. Defaults to 1.0. |
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no_repeat_ngram_size (int, optional): No repeat ngram size. Defaults to 0. |
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length_penalty (float, optional): Length penalty. Defaults to 1.0. |
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num_return_sequences (int, optional): Number of return sequences. Defaults to 1. |
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do_sample (bool, optional): Do sample. Defaults to False. |
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early_stopping (bool, optional): Early stopping. Defaults to False. |
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Returns: |
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torch.Tensor: lang_x with generated tokens appended to it |
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""" |
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num_beams = kwargs.pop("num_beams", 1) |
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if num_beams > 1: |
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vision_x = vision_x.repeat_interleave(num_beams, dim=0) |
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self.lang_encoder._use_cached_vision_x = True |
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self._encode_vision_x(vision_x=vision_x) |
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output = self.lang_encoder.generate( |
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input_ids=lang_x, |
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attention_mask=attention_mask, |
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num_beams=num_beams, |
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**kwargs, |
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) |
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self.lang_encoder.clear_conditioned_layers() |
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self.lang_encoder._use_cached_vision_x = False |
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return output |
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def _encode_vision_x(self, vision_x: torch.Tensor): |
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""" |
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Compute media tokens from vision input by passing it through vision encoder and conditioning language model. |
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Args: |
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vision_x (torch.Tensor): Vision input |
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shape (B, T_img, F, C, H, W) |
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Images in the same chunk are collated along T_img, and frames are collated along F |
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Currently only F=1 is supported (single-frame videos) |
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rearrange code based on https://github.com/dhansmair/flamingo-mini |
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""" |
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assert vision_x.ndim == 6, "vision_x should be of shape (b, T_img, F, C, H, W)" |
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b, T, F = vision_x.shape[:3] |
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assert F == 1, "Only single frame supported" |
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vision_x = rearrange(vision_x, "b T F c h w -> (b T F) c h w") |
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with torch.no_grad(): |
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module_to_inspect = unwrap_fsdp(self.vision_encoder) |
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if accepts_parameter(module_to_inspect.forward, "return_all_features"): |
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vision_x = self.vision_encoder(vision_x, return_all_features=True) |
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else: |
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vision_x = self.vision_encoder(vision_x)[1] |
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vision_x = rearrange(vision_x, "(b T F) v d -> b T F v d", b=b, T=T, F=F) |
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vision_x = self.perceiver(vision_x) |
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for layer in self.lang_encoder._get_decoder_layers(): |
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layer.condition_vis_x(vision_x) |
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def _condition_media_locations(self, input_ids: torch.Tensor): |
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""" |
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Compute the media token locations from lang_x and condition the language model on these. |
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Args: |
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input_ids (torch.Tensor): Language input |
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shape (B, T_txt) |
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""" |
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media_locations = input_ids == self.media_token_id |
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for layer in self.lang_encoder._get_decoder_layers(): |
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layer.condition_media_locations(media_locations) |
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def cache_media(self, input_ids: torch.Tensor, vision_x: torch.Tensor): |
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""" |
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Pre-cache a prompt/sequence of images / text for log-likelihood evaluations. |
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All subsequent calls to forward() will generate attending to the LAST |
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image in vision_x. |
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This is not meant to be used to cache things for generate(). |
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Args: |
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input_ids (torch.Tensor): Language input |
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shape (B, T_txt) |
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vision_x (torch.Tensor): Vision input |
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shape (B, T_img, F, C, H, W) |
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Images in the same chunk are collated along T_img, and frames are collated along F |
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Currently only F=1 is supported (single-frame videos) |
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""" |
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self._encode_vision_x(vision_x=vision_x) |
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self._condition_media_locations(input_ids=input_ids) |
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self.lang_encoder._use_cached_vision_x = True |
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def uncache_media(self): |
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""" |
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Clear all conditioning. |
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""" |
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self.lang_encoder.clear_conditioned_layers() |
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self.lang_encoder._use_cached_vision_x = False |
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